## Monday, November 14, 2016

### Limits of Linear Regression

For this weeks blog post, I have some thoughts on linear regression - in particular on the limits of using regression in the context of artificial intelligence.

To analyze the weaknesses of linear regression, it might be appropriate to talk about one of the strengths of regression first. The best thing that linear regression has going for it is simplicity. Anyone who has taken a course in algebra or basic statistics has enough knowledge to be able to understand the goal of this process. Given a set of observed data, try to describe what function produces it. Even in some of the more advanced topics such as fitting to multivarite functions, the basic idea of trying to create a function of best fit remains the same.

This simplicity, however, is also the main weakness of regression in that you need to be able to model the situation as a function. This isn’t a hard task in some fields where regression dominates, where you are gathering easily discretized data. There are no guarantees that you are going to be operating in a area with nice data. Even with a large bag of functions to fit to (exponential growth/decay, power law), you are still assuming that the function is well behaved. Not only does data have to be easy to dissect, it also has to be easy to digest.

In some ways, most of the algorithms we have talked about are the antithesis of linear regression. While before we had been striving for optimal solutions, in linear regression we are attempting to model the true solution, which will always be sub-optimal. The solutions error at any given point might be negligible, but how do you know? This is frustrating world we have stepped into to, we can only hope to have a good approximation to the solution.

I don’t actually think over-fitting is a huge problem with regression as it is a method to avoid overfitting error to begin with. Methods like Lagrange interpolation and cubic splines are much more aggressive. While over-fitting is a problem, it’s actually more operator error as opposed to fault with the method. This is more a problem of trying to throw all of the data at the equation without thinking about it.

And, I feel that is actually what people want to be able to do. Forget about the inherent logistical problems in managing large amounts of data, figuring out some way to sift through information and figure out whats important is just as much of a challenge. So while a neural network might not be as easily understandable, the ability to simply feed it all of your data is awesome. If the hardest part about dealing with the information you have is actually deciphering where to start, regression might be the wrong tool.

Don’t get me wrong, I think linear regression definitely has its place. I would personally use it over a neural network whenever I could get away with it.